Event-Based Vision for High-Speed Autonomous Navigation: Algorithms, Hardware, and Applications

Abstract

Event-based vision sensors, inspired by biological retinas, asynchronously detect per-pixel brightness changes with microsecond temporal resolution and high dynamic range, offering transformative potential for high-speed autonomous navigation where conventional frame-based cameras suffer from motion blur and limited frame rates. This paper comprehensively examines event-based vision algorithms, hardware platforms, and applications for unmanned systems operating in dynamic, high-speed scenarios. We systematically analyze the fundamental principles of event cameras including asynchronous pixel-level operation, sparse output encoding only brightness changes, and logarithmic intensity response enabling operation across extreme lighting conditions. The review categorizes algorithmic approaches for processing event streams: frame-based methods accumulating events into image-like representations for compatibility with conventional computer vision, event-based methods directly processing asynchronous data through spiking neural networks or time-surface representations, and hybrid approaches combining both paradigms. We examine key perception tasks including feature tracking exploiting high temporal resolution for robust correspondence, optical flow estimation from event timing information, object recognition through event-based convolutional networks, and simultaneous localization and mapping (SLAM) leveraging continuous motion information. Particular emphasis is placed on algorithms exploiting event camera advantages: high-speed motion estimation for fast-moving platforms, high dynamic range scene understanding in challenging lighting, and low-latency processing for reactive control. The paper analyzes hardware platforms spanning commercial event cameras (DVS, DAVIS, Prophesee), neuromorphic processors for efficient event processing (Loihi, TrueNorth), and hybrid systems combining event and frame-based sensors. Application-specific implementations are examined for high-speed UAV racing requiring rapid obstacle avoidance, space robotics with extreme lighting variations, and automotive scenarios with HDR requirements. We critically evaluate challenges including event noise in low-contrast scenes, calibration complexity, and limited availability of labeled training data. The review identifies research gaps in end-to-end learning frameworks and standardized benchmarks for event-based navigation.